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Volatility Cone & Analog Path Projection
Volatility Cone & Analog Path Projection — Forward Price Envelope with Fractal Replay and Terminal Probability Distribution
Overview
Nearly every overlay on TradingView describes the past: where price has been, where volume traded, where structure broke. This tool points in the other direction. It builds a forward projection zone from the current bar using three independent layers — a realized-volatility cone, a replay of the historically most similar price fractals, and a terminal probability profile that combines both into a distribution of possible outcomes at the projection horizon.
The result is not a forecast. It is a bounded expectation: a visual answer to "given how this instrument has actually been moving, what range is normal over the next N bars, and where has price historically ended up after conditions that looked like this?"
Conceptual Framework
Price uncertainty grows with the square root of time, not linearly. A 24-bar projection is not 24 times as wide as a 1-bar projection — it is roughly 4.9 times as wide. Traders who size targets and stops on a straight-line mental model consistently misjudge what is achievable in a given number of bars.
The cone makes that curvature visible. Its width at each future bar is sigma * sqrt(t), where sigma is the standard deviation of log returns over the volatility window. Three nested bands are drawn, so you can immediately see which targets sit inside the ordinary range, which sit at the statistical edge, and which would require an exceptional move.
The Gaussian model alone, however, is a poor description of real markets: returns have fat tails, and volatility clusters. The analog layer addresses this by ignoring models entirely and asking an empirical question instead — what actually happened, historically, after the market printed this exact shape?
How It Works
Volatility estimation. Log returns are computed bar to bar. Their standard deviation over the volatility window gives the per-bar sigma; their mean gives the drift. Drift can be included or excluded from the cone's centerline.
Cone construction. For each future bar t from 1 to the horizon, the upper and lower bounds are close * exp(drift*t ± k*sigma*sqrt(t)) for each of the three band multipliers. Each band is rendered as a closed polygon with layered transparency, producing depth from the centerline outward.
Fingerprint extraction. The most recent N bars of log returns are z-scored — mean removed, divided by their own standard deviation. This makes the pattern scale-invariant: the same shape is recognised whether it happened during a quiet range or a volatile expansion, and at any price level.
Historical scan. Every candidate window inside the scan depth is z-scored the same way and compared to the current fingerprint by summed squared difference. Lower distance means a closer shape match. Candidates that overlap an already-selected match without improving on it are rejected, so the top results are not five copies of the same event shifted by one bar.
Forward replay. For each of the top matches, the bars that followed it are converted into a relative path and re-anchored to the current close. The path each analog is drawing forward is exactly the move that occurred after that historical fingerprint — nothing is fitted or optimised. Paths ending above the current price are drawn bullish, below bearish, and a thick median line traces the bar-by-bar median across all analogs.
Terminal probability profile. At the projection horizon a horizontal distribution is built across the cone's full range. Each row's density blends the Gaussian probability implied by the volatility model with an empirical kernel centred on each analog's endpoint. The Model Weight input controls that mix: 1.0 is purely theoretical, 0.0 is purely historical, and the default sits between them. The widest row — the mode of the blended distribution — is marked as the most probable zone.
Interpretation
Cone bands define what is statistically ordinary. A target beyond the outer band within the horizon is not impossible, it is simply rare — treat it accordingly when planning holding time.
Cone width itself is information. A narrow cone means compressed volatility, which historically resolves into expansion. A wide cone means the market is already moving; chasing inside it carries a worse risk profile.
Analog dispersion matters more than analog direction. Five paths that fan out in all directions means the current shape carried no historical edge. Five paths clustering in one direction is the meaningful configuration.
Best Match Quality in the panel scores how closely the nearest historical fingerprint resembles the present one. Below roughly 60%, treat the analog layer as noise and rely on the cone alone.
The most probable zone is where the blended distribution peaks. It is a magnet-style reference, not a target — the distribution is wide by construction.
Volatility Regime compares short-window volatility to the full window. Expanding means the cone is likely to understate near-term movement; contracting means the opposite.
Settings
Setting Effect
Projection Horizon Bars projected forward. Also the endpoint of the profile
Volatility Window Sample size for sigma and drift. Longer = smoother, slower to adapt
Include Drift Tilts the cone with the window's mean return
Inner / Mid / Outer Band Sigma multipliers for the three layers
Fingerprint Length Bars compared for similarity. Shorter = more matches, less specific
Scan Depth How far back to search for analogs
Number of Analogs How many historical paths to replay
Profile Rows / Width Resolution and horizontal size of the terminal distribution
Model Weight Gaussian versus empirical blend in the distribution
Redraw on Bar Close Only Recommended on. The scan is heavy; this runs it once per bar
Limitations — read this
This is not a prediction and must not be traded as one. The cone describes a statistical range under an assumption of stable volatility. Real volatility is not stable, and returns have fatter tails than the Gaussian model implies, so moves outside the outer band occur more often than the model suggests.
Analog matching is weak evidence. A few dozen bars of shape similarity is a small sample; markets are non-stationary and a pattern that resolved one way in the past carries no obligation to repeat. The paths are historical context, not a probability statement about the future.
Nothing repaints, but the whole projection is recomputed each bar. Yesterday's cone is not preserved — the drawing always reflects current data only. It is anchored to the last bar by design.
On low-volume, illiquid, or heavily gapped instruments the return distribution is distorted and both layers degrade.
No entries, no stops, no targets, no signals. This is a context tool for sizing expectations and holding time.
Overview
Nearly every overlay on TradingView describes the past: where price has been, where volume traded, where structure broke. This tool points in the other direction. It builds a forward projection zone from the current bar using three independent layers — a realized-volatility cone, a replay of the historically most similar price fractals, and a terminal probability profile that combines both into a distribution of possible outcomes at the projection horizon.
The result is not a forecast. It is a bounded expectation: a visual answer to "given how this instrument has actually been moving, what range is normal over the next N bars, and where has price historically ended up after conditions that looked like this?"
Conceptual Framework
Price uncertainty grows with the square root of time, not linearly. A 24-bar projection is not 24 times as wide as a 1-bar projection — it is roughly 4.9 times as wide. Traders who size targets and stops on a straight-line mental model consistently misjudge what is achievable in a given number of bars.
The cone makes that curvature visible. Its width at each future bar is sigma * sqrt(t), where sigma is the standard deviation of log returns over the volatility window. Three nested bands are drawn, so you can immediately see which targets sit inside the ordinary range, which sit at the statistical edge, and which would require an exceptional move.
The Gaussian model alone, however, is a poor description of real markets: returns have fat tails, and volatility clusters. The analog layer addresses this by ignoring models entirely and asking an empirical question instead — what actually happened, historically, after the market printed this exact shape?
How It Works
Volatility estimation. Log returns are computed bar to bar. Their standard deviation over the volatility window gives the per-bar sigma; their mean gives the drift. Drift can be included or excluded from the cone's centerline.
Cone construction. For each future bar t from 1 to the horizon, the upper and lower bounds are close * exp(drift*t ± k*sigma*sqrt(t)) for each of the three band multipliers. Each band is rendered as a closed polygon with layered transparency, producing depth from the centerline outward.
Fingerprint extraction. The most recent N bars of log returns are z-scored — mean removed, divided by their own standard deviation. This makes the pattern scale-invariant: the same shape is recognised whether it happened during a quiet range or a volatile expansion, and at any price level.
Historical scan. Every candidate window inside the scan depth is z-scored the same way and compared to the current fingerprint by summed squared difference. Lower distance means a closer shape match. Candidates that overlap an already-selected match without improving on it are rejected, so the top results are not five copies of the same event shifted by one bar.
Forward replay. For each of the top matches, the bars that followed it are converted into a relative path and re-anchored to the current close. The path each analog is drawing forward is exactly the move that occurred after that historical fingerprint — nothing is fitted or optimised. Paths ending above the current price are drawn bullish, below bearish, and a thick median line traces the bar-by-bar median across all analogs.
Terminal probability profile. At the projection horizon a horizontal distribution is built across the cone's full range. Each row's density blends the Gaussian probability implied by the volatility model with an empirical kernel centred on each analog's endpoint. The Model Weight input controls that mix: 1.0 is purely theoretical, 0.0 is purely historical, and the default sits between them. The widest row — the mode of the blended distribution — is marked as the most probable zone.
Interpretation
Cone bands define what is statistically ordinary. A target beyond the outer band within the horizon is not impossible, it is simply rare — treat it accordingly when planning holding time.
Cone width itself is information. A narrow cone means compressed volatility, which historically resolves into expansion. A wide cone means the market is already moving; chasing inside it carries a worse risk profile.
Analog dispersion matters more than analog direction. Five paths that fan out in all directions means the current shape carried no historical edge. Five paths clustering in one direction is the meaningful configuration.
Best Match Quality in the panel scores how closely the nearest historical fingerprint resembles the present one. Below roughly 60%, treat the analog layer as noise and rely on the cone alone.
The most probable zone is where the blended distribution peaks. It is a magnet-style reference, not a target — the distribution is wide by construction.
Volatility Regime compares short-window volatility to the full window. Expanding means the cone is likely to understate near-term movement; contracting means the opposite.
Settings
Setting Effect
Projection Horizon Bars projected forward. Also the endpoint of the profile
Volatility Window Sample size for sigma and drift. Longer = smoother, slower to adapt
Include Drift Tilts the cone with the window's mean return
Inner / Mid / Outer Band Sigma multipliers for the three layers
Fingerprint Length Bars compared for similarity. Shorter = more matches, less specific
Scan Depth How far back to search for analogs
Number of Analogs How many historical paths to replay
Profile Rows / Width Resolution and horizontal size of the terminal distribution
Model Weight Gaussian versus empirical blend in the distribution
Redraw on Bar Close Only Recommended on. The scan is heavy; this runs it once per bar
Limitations — read this
This is not a prediction and must not be traded as one. The cone describes a statistical range under an assumption of stable volatility. Real volatility is not stable, and returns have fatter tails than the Gaussian model implies, so moves outside the outer band occur more often than the model suggests.
Analog matching is weak evidence. A few dozen bars of shape similarity is a small sample; markets are non-stationary and a pattern that resolved one way in the past carries no obligation to repeat. The paths are historical context, not a probability statement about the future.
Nothing repaints, but the whole projection is recomputed each bar. Yesterday's cone is not preserved — the drawing always reflects current data only. It is anchored to the last bar by design.
On low-volume, illiquid, or heavily gapped instruments the return distribution is distorted and both layers degrade.
No entries, no stops, no targets, no signals. This is a context tool for sizing expectations and holding time.
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秉持TradingView一貫精神，這個腳本的創作者將其設為開源，以便交易者檢視並驗證其功能。向作者致敬！您可以免費使用此腳本，但請注意，重新發佈代碼需遵守我們的社群規範。
Trade in your language. AlgoForex Global in English, Chinese, Spanish, Arabic and French: free indicators, MT5 bots, Education live signals, market news, economic calendar, academy and 50+ trading tools. algoforexglobal.com/
免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。
開源腳本
秉持TradingView一貫精神，這個腳本的創作者將其設為開源，以便交易者檢視並驗證其功能。向作者致敬！您可以免費使用此腳本，但請注意，重新發佈代碼需遵守我們的社群規範。
Trade in your language. AlgoForex Global in English, Chinese, Spanish, Arabic and French: free indicators, MT5 bots, Education live signals, market news, economic calendar, academy and 50+ trading tools. algoforexglobal.com/
免責聲明
這些資訊和出版物並非旨在提供，也不構成TradingView提供或認可的任何形式的財務、投資、交易或其他類型的建議或推薦。請閱讀使用條款以了解更多資訊。